The ever-increasing demand for training and inferring with larger machine-learning models requires more efficient hardware solutions due to limitations such as power dissipation and scalability. Optics is a promising contender for providing lower power computation, since light propagation through a nonabsorbing medium is a lossless operation. However, to carry out useful and efficient computations with light, generating and controlling nonlinearity optically is a necessity that is still elusive. Multimode fibers (MMFs) have been shown that they can provide nonlinear effects with microwatts of average power while maintaining parallelism and low loss. We propose an optical neural network architecture that performs nonlinear optical computation by controlling the propagation of ultrashort pulses in MMF by wavefront shaping. With a surrogate model, optimal sets of parameters are found to program this optical computer for different tasks with minimal utilization of an electronic computer. We show a remarkable decrease of 97% in the number of model parameters, which leads to an overall 99% digital operation reduction compared to an equivalently performing digital neural network. We further demonstrate that a fully optical implementation can also be performed with competitive accuracies.
In the field of machine learning, large datasets are essential for heavy tasks. However, the performance of power-hungry processors is limited by the data transfer to and from memory. Optical computing has been gaining interest as a means of high-speed computation, and here we present an optical computing framework called scalable optical learning operator based on spatiotemporal effects in multimode fibers. This framework is capable of performing various learning tasks, such as classifying COVID-19 X-ray lung images, speech recognition, and age prediction from face images. Our approach addresses the energy scaling problem without compromising speed by leveraging the simultaneous, linear and nonlinear interaction of spatial modes as a computation engine. Our experiments demonstrate the accuracy of our method comparable to a digital implementation.
Utilizing light propagation and optical nonlinearities is one of the strategies to accelerate computational tasks in tandem with electrical circuits in an energy-efficient manner. Computing with multimode optical fibers has been demonstrated to be energy efficient due to the high light confinement and multidimensionality. However, these optical nonlinearities have not been programmed for a specific computational task and thus the performance is not optimal. In this study, we demonstrate that the nonlinear transformation in the fiber can be programmed to obtain improved performances on several different machine learning tasks by shaping the wavefront of the information encoding beam.
An optical computing framework based on spatiotemporal nonlinear effects of multimode fibers is presented. Experimentally, a powerful computation engine can be realized using linear and nonlinear interactions of spatial fiber modes. With the present optical scheme, we demonstrated excellent performance on a variety of classification and regression tasks. Our studies showed that spatiotemporal fiber nonlinearities are as effective as digital neural network structures in challenging computational tasks. Featuring better energy efficiency and easy scalability, our method provides a new approach to optical computation.
We propose and demonstrate the use of multimode fibers (MMF) inside a laser cavity as a new path to generate spatiotemporal modelocked pulses with high beam quality and high energy. Prior to our work, MMFs in optical cavities resulted in the generation of low-quality output beam profiles by spatiotemporal mode-locking. Here we present a versatile approach to reach high energy per pulse directly in the mode-locked MMF oscillator with a near single-mode output beam profile. Our approach relies on spatial beam self-cleaning via the nonlinear Kerr effect inside the cavity achieved by controlling spatiotemporal pulse propagation with a dispersion-managed design. We demonstrate the versatility of our approach with Yb-doped and Er-doped multimode laser cavities which generate pulse energies of 24 nJ and 16 nJ, respectively. The high peak power reached in the MMF within the cavity induced a Kerr self-beam cleaning which produced a near Gaussian mode output (M2<1.13).
A novel optical computing framework is presented by harnessing spatiotemporal nonlinear effects of multimode fibers for machine learning. With linear and nonlinear interactions of spatial fiber modes, a powerful computation engine is experimentally realized. We demonstrated excellent performance with the present optical scheme for various classification tasks. We demonstrated that spatiotemporal fiber nonlinearities perform as well as digital neural network structures for challenging computational tasks. With better energy efficiency and easy scalability, our method presents a novel path toward powerful optical computation.
We propose an imaging method for controlling the output of scattering media such as multimode fibers using machine learning. Arbitrary images can be projected with amplitude-only calibration (no phase measurement) and fidelities on par with conventional full-measurement methods.
The performance of fiber mode-locked lasers is limited due to the high nonlinearity induced by the spatial confinement of the single-mode fiber core. To massively increase the pulse energy of the femtosecond pulses, amplification is performed outside the oscillator. Recently, spatiotemporal mode-locking has been proposed as a new path to fiber lasers. However, the beam quality was highly multimode, and the calculated threshold pulse energy (>100 nJ) for nonlinear beam self-cleaning was challenging to realize. We present an approach to reach high energy per pulse directly in the mode-locked multimode fiber oscillator with a near single-mode output beam. Our approach relies on spatial beam self-cleaning via the nonlinear Kerr effect, and we demonstrate a multimode fiber oscillator with M2 < 1.13 beam profile, up to 24 nJ energy, and sub-100 fs compressed duration. Nonlinear beam self-cleaning is verified both numerically and experimentally for the first time in a mode-locked multimode laser cavity. The reported approach is further power scalable with larger core sized fibers up to a certain level of modal dispersion and could benefit applications that require high-power ultrashort lasers with commercially available optical fibers.
We present the first spatiotemporally mode-locked fiber laser with self-similar pulse evolution, to the best of our knowledge. Our multimode fiber laser produces amplifier similaritons with near-Gaussian beam quality (M2<1.4) at the output. Ytterbium based laser generates 2.3 ps pulses at 1030 nm with 2.4 nJ energy. The output pulses are externally compressed to 192 fs with a grating compressor. Intracavity large spectral breathing (>6) and less chirped pulses than the cavity induced total dispersion are the verifications of the spatiotemporal self-similar pulse propagation.
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